Note. Boxplots display the interquartile range (IQR, center box), and the whiskers extend 1.5*IQR from the lower and upper hinge. The white point indicates the mean and the white center line indicates the median.


Import Data

In a first step we import the raw Qualtrics data, which was downloaded as an SPSS file.

# Reset working directory to folder current file is saved in
#setwd(dirname(rstudioapi::getActiveDocumentContext()$path))

# Import Qualtrics Survey Data
dt0Raw <- read_spss("data/raw data/Snowball+Corona+Long-Term+Coverage+-+Baseline_March+30,+2020_00.32.sav")

The raw data set includes 588 variables for 14401 cases.

Data Quality

Missing Data

Inspecting missing data in the items.

# Table: Missing Data per item
dt0Raw %>%
  select(-starts_with("t_"), -starts_with("Pol")) %>% #drop timers and Political orientation (because of translation missingness)
  select_if(~sum(is.na(.)) > 0) %>% # remove all variables that have no missingess
  naniar::miss_var_summary(.) %>% # by variable summary of missingness proportion
  DT::datatable(.,
                colnames = c("Variable", "Number Missing", "Percentage Missing"),
                filter = 'top',
                extensions = 'Buttons',
                options = list(
                  columnDefs = list(list(className = 'dt-center')),
                  #autoWidth = TRUE,
                  dom = 'Bfrtlip',
                  buttons = c('copy', 'csv', 'excel', 'pdf', 'print'))) %>%
  DT::formatRound('pct_miss', digits = 2)
# Plot: Missing Data per item
dt0Raw %>%
  select(-starts_with("t_"), -starts_with("Pol")) %>% #drop timers and Political orientation (because of translation missingness)
  select_if(~sum(is.na(.)) > 0) %>% # remove all variables that have no missingess
  naniar::gg_miss_var(.) # visualize by variable summary of missingness proportion

# Plot: Missing Data cumulative
dt0Raw %>%
  select(-starts_with("t_"), -starts_with("Pol")) %>% #drop timers and Political orientation (because of translation missingness)
  select_if(~sum(is.na(.)) > 0) %>% # remove all variables that have no missingess
  naniar::gg_miss_var_cumsum(.) # missingness development over survey

# Co-occurences of missingess - too many variables
#dt0Raw %>%
#  select(-starts_with("t_"), -starts_with("Pol")) %>% #drop timers and Political orientation (because of translation missingness)
#  select_if(~sum(is.na(.)) > 0) %>% # remove all variables that have no missingess
#  naniar::gg_miss_upset(., nsets = n_var_miss(.)) # visualize missingess co-occurences

Filter: Preview Responses

Filter the Preview responses.

# flag Preview Responses
dt1Preview <- dt0Raw %>%
  mutate(FilterPreview = labelled(ifelse(Status == 0,0,1),
                                  labels = c(preview = 1), label="Filter: survey preview response"))

Filter: Short Duration on Survey

Filter survey responses that were shorter than 10 minutes.

# truncate data:
tOutlierHigh <- dt1Preview %>%
  select(Duration__in_seconds_) %>%
  filter(Duration__in_seconds_<=stats::median(Duration__in_seconds_)+stats::mad(Duration__in_seconds_)*3.5) %>%
  mutate(Minutes = Duration__in_seconds_/60)

# set time cut-off criterion:
tCutOff <- 10 #cut-off criterion in minutes
tCutOffPerc <- round(sum(tOutlierHigh$Minutes<tCutOff)/nrow(dt1Preview)*100,2) # percent of missing data with current cut-off criterion
tOutlierHigh$out <- tOutlierHigh$Minutes < 10

# plot histogram and missing
ggplot(data=tOutlierHigh, aes(x=Minutes, fill=out)) +
  geom_histogram(bins=round(max(tOutlierHigh$Minutes),0),
                 alpha=.6) +
  geom_vline(xintercept = tCutOff, 
             color = "darkred",
             linetype = "longdash") +
  geom_text(aes(x=tCutOff, label=paste0("time cut-off: ",tCutOff," Minutes\n"), y=Inf), 
            hjust = 1,
            colour="darkred", 
            angle=90) +
  geom_text(aes(x=tCutOff, label=paste0("\ndata loss: ",tCutOffPerc,"%"), y=Inf), 
            hjust = 1,
            colour="darkred", 
            angle=90) +
  scale_x_continuous(breaks = seq(0, round(max(tOutlierHigh$Minutes),0), 5)) +
  scale_fill_manual(values=c("darkgrey","darkred")) +
  labs(title = "Truncated Histogram: Survey Duration", 
       x = "Duration [Mintues]",
       y = "Frequency Count",
       caption = "Notes:
       (1) Truncated: all participants who took less time than Median+3.5*MAD
       (2) Each bin represents one Minute") +
  theme_Publication() +
  theme(legend.position = "none")

rm(tOutlierHigh, tCutOff, tCutOffPerc)

# flag anyone with less than 10 minutes survey duration
dt2Time <- dt1Preview %>%
  mutate(FilterTime = labelled(ifelse(Duration__in_seconds_ > 600,0,1),
                               labels = c(`extremely quick` = 1), label="Filter: Took less than 10 minutes on survey"))

Filter: Straightliners

Filter participants, who have straightlined on the job insecurity scale, which includes a reverse coded item. We only flag people who straightlined outside the median categories because all “neither agree nor disagree” might be meaningful response.

# CheckMissingness pattern
naniar::gg_miss_upset(dt2Time %>%
  select(ResponseId, jbInsec01, jbInsec02, jbInsec03) %>%
  na_if(., -99) # all -99 into <NA>
)

# isolate respondents who have straightlined outside a the median categories (b/c all "neither agree nor disagree" might be meaningful response) 
jobinsecRed <- dt2Time %>%
  select(ResponseId, jbInsec01, jbInsec02, jbInsec03) %>%
  na_if(., -99) %>% # all -99 into <NA>
  na.omit() %>% # remove people who have missing data on one of the three items
  mutate(mean = rowMeans(select(., c("jbInsec01", "jbInsec02", "jbInsec03"))), 
         sd = matrixStats::rowSds(as.matrix(select(., c("jbInsec01", "jbInsec02", "jbInsec03"))))) %>% # calculate row-means and row-sds 
  filter(sd == 0, mean != 0)

# flag anyone who straightlined on job insecurity
dt3Straightliner <- dt2Time %>%
  mutate(FilterStraightliner = labelled(ifelse(!ResponseId %in% jobinsecRed$ResponseId,0,1),
                                        labels = c(straightliner = 1), label="Filter: straightliner on Job Insecurity"))
rm(jobinsecRed)

Data Preparation

Note: For each of the scales we do an item analysis, and combine the items to the mean- (.m), and factor scores (.fa). We also centered (.c) and standardized (.z) the mean scores. Most of these items are not labelled for SPSS yet.

Recoded Items

Re-coding reverse coded items and the Qualtrics language codes.

# Recoded Items
dt4newVars <- dt3Straightliner %>%
  mutate(jbInsec02_R = labelled(recode(as.numeric(jbInsec02), `-2` = 2, `-1` = 1, `0` = 0, `1` = -1, `2` = -2, `-99` = -99),
                                labels = NULL, label="Job Insecurity 02 (re-coded)"),
         bor03_R = labelled(recode(as.numeric(bor03), `-2` = 2, `-1` = 1, `0` = 0, `1` = -1, `2` = -2),
                            labels = NULL, label="Boredom 03 (re-coded)"))
# Language
# Import Qualtrics Language Codes
qualtricsLanguage <- read_excel("data/raw data/qualtricsLanguageCodes.xlsx")
dt4newVars <- merge(x=dt4newVars, y=qualtricsLanguage, by="Q_Language", all.x=TRUE)
rm(qualtricsLanguage)

Country

We currently have 712 different free text country responses. Here we aim to consolidate them into one variable.

#code originally provided by Courtney Soderberg
dt4newVars <- dt4newVars %>%
  mutate(coded_country = case_when(grepl('^usa$', country, ignore.case = T) | grepl('unites state', country, ignore.case = T) | 
                                     grepl('united state', country, ignore.case = T) | grepl('^america$', country, ignore.case = T) |
                                     grepl('U\\.S\\.', country, ignore.case = T) | grepl('Estados Unidos', country, ignore.case = T) |
                                     grepl('colorado', country, ignore.case = T) | grepl('^us$', country, ignore.case = T) |
                                     grepl('xas', country, ignore.case = T) | grepl('sates', country, ignore.case = T) |
                                     grepl('Amerika Serikat', country, ignore.case = T) | grepl('california', country, ignore.case = T) |
                                     grepl('corlifornia', country, ignore.case = T) | grepl('états-unis', country, ignore.case = T) |
                                     grepl('york', country, ignore.case = T) | grepl('yark', country, ignore.case = T) |
                                     grepl('puerto rico', country, ignore.case = T) | grepl('^tx$', country, ignore.case = T) |
                                     grepl('^tn$', country, ignore.case = T) | grepl('U S', country, ignore.case = T) ~ 'United States of America',
                                   grepl('canad', country, ignore.case = T) | grepl('vancouver', country, ignore.case = T) ~ 'Canada',
                                   grepl('mexico', country, ignore.case = T) | grepl('México', country, ignore.case = T) ~ 'Mexico',
                                   grepl('spain', country, ignore.case = T) | grepl('esp', country, ignore.case = T) |
                                     grepl('Spagna', country, ignore.case = T) | grepl('Spanien', country, ignore.case = T) |
                                     grepl('Catal', country, ignore.case = T) | grepl('Euskal Herria', country, ignore.case = T) |
                                     grepl('basque', country, ignore.case = T) | grepl('Eapaña', country, ignore.case = T) |
                                     grepl('Esapaña', country, ignore.case = T) | grepl('madrid', country, ignore.case = T) |
                                     grepl('Montalbán de Córdoba', country, ignore.case = T) | grepl('Pais vasco', country, ignore.case = T) |
                                     grepl('Spanje', country, ignore.case = T) ~ 'Spain',
                                   grepl('france', country, ignore.case = T) | grepl('Francia', country, ignore.case = T) |
                                     grepl('Frankrijk', country, ignore.case = T) ~ 'France',
                                   grepl('germany', country, ignore.case = T) | grepl('deutschland', country, ignore.case = T) | 
                                     grepl('Alemania', country, ignore.case = T) | grepl('germania', country, ignore.case = T) |
                                     grepl('^Almanya$', country, ignore.case = T) | grepl('berlin', country, ignore.case = T) |
                                     grepl('Duitsland', country, ignore.case = T) ~ 'Germany',
                                   grepl('portugal', country, ignore.case = T) ~ 'Portugal',
                                   grepl('weden', country, ignore.case = T) ~ 'Sweden',
                                   grepl('netherland', country, ignore.case = T) | grepl('nederland', country, ignore.case = T) | 
                                     grepl('Niederlande', country, ignore.case = T) | grepl('Belanda', country, ignore.case = T) |
                                     grepl('^NL$', country, ignore.case = T) | grepl('Olanda', country, ignore.case = T) |
                                     grepl('Paesi Bassi', country, ignore.case = T) | grepl('bajos', country, ignore.case = T) |
                                     grepl('Gelderland', country, ignore.case = T) | grepl('Hollanda', country, ignore.case = T) ~ 'Netherlands',
                                   grepl('^indonesia$', country, ignore.case = T) | grepl('indonesian', country, ignore.case = T) | 
                                     grepl('kota Tarakan', country, ignore.case = T) | grepl('Imdonesia', country, ignore.case = T) |
                                     grepl('Indònesia', country, ignore.case = T) | grepl('jakarta', country, ignore.case = T) ~ 'Indonesia',
                                   grepl('ital', country, ignore.case = T) | grepl('Sardegna', country, ignore.case = T) | 
                                     grepl('Bisceglie', country, ignore.case = T) | grepl('Ladispoli', country, ignore.case = T) |
                                     grepl('Castelforte', country, ignore.case = T) | grepl('milano', country, ignore.case = T) |
                                     (grepl('roma', country, ignore.case = T) & !grepl('romania', country, ignore.case = T)) |
                                     grepl('Dorgali', country, ignore.case = T) | grepl('bari', country, ignore.case = T) |
                                     grepl('bologna', country, ignore.case = T) | grepl('Brescia', country, ignore.case = T) |
                                     grepl('Cala gonone', country, ignore.case = T) | grepl('Chieti', country, ignore.case = T) |
                                     grepl('Ferentino', country, ignore.case = T) | grepl('Frosinone', country, ignore.case = T) |
                                     grepl('Gragnano lucca  ', country, ignore.case = T) | grepl('Guidonia', country, ignore.case = T) |
                                     grepl('Itaia', country, ignore.case = T) | grepl('İtalya', country, ignore.case = T) |
                                     grepl('Mareno di Piave', country, ignore.case = T) | grepl('modena', country, ignore.case = T) |
                                     grepl('Pellizzano', country, ignore.case = T) | grepl('Predazzo', country, ignore.case = T) |
                                     grepl('Refrontolo', country, ignore.case = T) | grepl('Cosma e Damiano', country, ignore.case = T) |
                                     grepl('Scalea', country, ignore.case = T) | grepl('Scauri', country, ignore.case = T) |
                                     grepl('Segni', country, ignore.case = T) | grepl('SETTIMO VITTONE', country, ignore.case = T) |
                                     grepl('Susegana', country, ignore.case = T) | grepl('Terralba', country, ignore.case = T) |
                                     grepl('trento', country, ignore.case = T) | grepl('treviso', country, ignore.case = T) |
                                     grepl('Tezze di Piave', country, ignore.case = T) | grepl('Valmontone', country, ignore.case = T) |
                                     grepl('Vergato', country, ignore.case = T) | grepl('veneto', country, ignore.case = T) |
                                     grepl('Gragnano lucca', country, ignore.case = T) ~ 'Italy',
                                   grepl('hong kong', country, ignore.case = T) ~ 'Hong Kong S.A.R.',
                                   grepl('phil', country, ignore.case = T) | grepl('Filipinas', country, ignore.case = T) ~ 'Philippines',
                                   grepl('argentina', country, ignore.case = T) | grepl('arge', country, ignore.case = T) ~ 'Argentina',
                                   grepl('pakistan', country, ignore.case = T) | grepl('Abbottabad', country, ignore.case = T) |
                                     grepl('Peshawar', country, ignore.case = T) ~ 'Pakistan',
                                   grepl('united kingdo', country, ignore.case = T) | grepl('^uk$', country, ignore.case = T) |
                                     grepl('Reino Unido', country, ignore.case = T) | grepl('britain', country, ignore.case = T) |
                                     grepl('Regno Unito', country, ignore.case = T) | grepl('u\\.k\\.', country, ignore.case = T) |
                                     grepl('بريطانيا', country, ignore.case = T) | grepl('the uk', country, ignore.case = T) |
                                     grepl('U K', country, ignore.case = T) | grepl('Verenigd Koninkrijk', country, ignore.case = T) |
                                     grepl('Windsor', country, ignore.case = T) | grepl('scotland', country, ignore.case = T) |
                                     grepl('england', country, ignore.case = T) | grepl('wales', country, ignore.case = T) |
                                     grepl('İngiltere', country, ignore.case = T) | grepl('Northern Ireland', country, ignore.case = T) |
                                     grepl('Egland', country, ignore.case = T) | grepl('^gb$', country, ignore.case = T) |
                                     grepl('N Ireland', country, ignore.case = T) | grepl('Schotland', country, ignore.case = T) |
                                     grepl('Scozia', country, ignore.case = T) ~ 'United Kingdom',
                                   grepl('africa', country, ignore.case = T) | grepl('^SA$', country, ignore.case = T) |
                                     grepl('Sudáfrica', country, ignore.case = T) | grepl('western cape', country, ignore.case = T) ~ 'South Africa',
                                   grepl('^chile$', country, ignore.case = T) ~ 'Chile',
                                   grepl('australia', country, ignore.case = T) | grepl('Austrija', country, ignore.case = T) ~ 'Australia',
                                   grepl('colombia', country, ignore.case = T) ~ 'Colombia',
                                   grepl('turkey', country, ignore.case = T) | grepl('tür', country, ignore.case = T) ~ 'Turkey',
                                   grepl('taiwan', country, ignore.case = T) ~ 'Taiwan',
                                   grepl('^Venezuela$', country, ignore.case = T) ~ 'Venezuela',
                                   grepl('israel', country, ignore.case = T) | grepl('اللد', country, ignore.case = T) |
                                     grepl('اسرائيل', country, ignore.case = T) | grepl('كفر قاسم', country, ignore.case = T) |
                                     grepl('Isreal', country, ignore.case = T) | grepl('רמלה', country, ignore.case = T) ~ 'Israel',
                                   grepl('greece', country, ignore.case = T) | grepl('Grecia', country, ignore.case = T) ~ 'Greece',
                                   grepl('austria', country, ignore.case = T) | grepl('sterreich', country, ignore.case = T) ~ 'Austria',
                                   grepl('new zealand', country, ignore.case = T) | grepl('Neuseeland', country, ignore.case = T) ~ 'New Zealand',
                                   grepl('Tuni', country, ignore.case = T) | grepl('تونس', country, ignore.case = T) ~ 'Tunisia',
                                   grepl('Belg', country, ignore.case = T) | grepl('Bélgica', country, ignore.case = T) ~ 'Belgium',
                                   grepl('China', country, ignore.case = T) ~ 'China',
                                   grepl('cyp', country, ignore.case = T) ~ 'Cyprus',
                                   grepl('Schweiz', country, ignore.case = T) | grepl('Suiza', country, ignore.case = T) |
                                     grepl('Svizzera', country, ignore.case = T) | grepl('Zwitserland', country, ignore.case = T) |
                                     grepl('switzerland', country, ignore.case = T) ~ 'Switzerland',
                                   grepl('United Arab Emirates', country, ignore.case = T) | grepl('uae', country, ignore.case = T) ~ 'United Arab Emirates',
                                   grepl('Croa', country, ignore.case = T) ~ 'Croatia',
                                   grepl('india', country, ignore.case = T) ~ 'India',
                                   grepl('algeri', country, ignore.case = T) | grepl('الجزائر', country, ignore.case = T) |
                                     grepl('Algérie', country, ignore.case = T) ~ 'Algeria',
                                   grepl('bulgaria', country, ignore.case = T) ~ 'Bulgaria',
                                   grepl('Poland', country, ignore.case = T) | grepl('POLONIA', country, ignore.case = T) ~ 'Poland',
                                   grepl('romania', country, ignore.case = T) ~ 'Romania',
                                   grepl('singapore', country, ignore.case = T) ~ 'Singapore',
                                   grepl('Srbija', country, ignore.case = T) | grepl('serbia', country, ignore.case = T) |
                                     grepl('Србија', country, ignore.case = T) ~ 'Republic of Serbia',
                                   grepl('czech', country, ignore.case = T) | grepl('checa', country, ignore.case = T) ~ 'Czech Republic',
                                   grepl('lux', country, ignore.case = T) ~ 'Luxembourg',
                                   grepl('slova', country, ignore.case = T) ~ 'Slovakia',
                                   grepl('brazil', country, ignore.case = T) | grepl('brasil', country, ignore.case = T)~ 'Brazil',
                                   grepl('^ireland$', country, ignore.case = T) | grepl('Irlanda', country, ignore.case = T) ~ 'Ireland',
                                   grepl('japan', country, ignore.case = T) | grepl('Giappone', country, ignore.case = T) |
                                     grepl('Japonya', country, ignore.case = T) ~ 'Japan',
                                   grepl('Malay', country, ignore.case = T) ~ 'Malaysia',
                                   grepl('nigeria', country, ignore.case = T) ~ 'Nigeria',
                                   grepl('Riyad', country, ignore.case = T) | grepl('^Saudi arabia$', country, ignore.case = T) |
                                     grepl('Arabia Saudita', country, ignore.case = T) | grepl('^saudi$', country, ignore.case = T) |
                                     grepl('Kingdom of Saudia arabia', country, ignore.case = T) | grepl('KSA', country, ignore.case = T) |
                                     grepl('k\\.s\\.a', country, ignore.case = T) | grepl('Arabie saoudite', country, ignore.case = T) | 
                                     grepl('الرياض', country, ignore.case = T) | grepl('السعودية', country, ignore.case = T) |
                                     grepl('السعوديه', country, ignore.case = T) ~ 'Saudi Arabia',
                                   grepl('^thailand$', country, ignore.case = T) ~ 'Thailand',
                                   grepl('urug', country, ignore.case = T) ~ 'Uruguay',
                                   grepl('costa', country, ignore.case = T) ~ 'Costa Rica', 
                                   grepl('ecuador', country, ignore.case = T) ~ 'Ecuador',
                                   grepl('finland', country, ignore.case = T) ~ 'Finland', 
                                   grepl('guat', country, ignore.case = T) ~ 'Guatemala',
                                   grepl('iceland', country, ignore.case = T) ~ 'Iceland',
                                   grepl('iraq', country, ignore.case = T) | grepl('العراق', country, ignore.case = T) ~ 'Iraq',
                                   grepl('iran', country, ignore.case = T) ~ 'Iran',
                                   grepl('lebanon', country, ignore.case = T) | grepl('liban', country, ignore.case = T) ~ 'Lebanon',
                                   grepl('norway', country, ignore.case = T) ~ 'Norway',
                                   grepl('palestine', country, ignore.case = T) | grepl('فلسطين ', country, ignore.case = T) |
                                     grepl('^فلسطين$', country, ignore.case = T) | grepl('الرملة', country, ignore.case = T) ~ 'Palestine',
                                   grepl('peru', country, ignore.case = T) ~ 'Peru',
                                   grepl('domin', country, ignore.case = T) ~ 'Dominican Republic',
                                   grepl('albania', country, ignore.case = T) ~ 'Albania',
                                   grepl('andorra', country, ignore.case = T) ~ 'Andorra',
                                   grepl('bahrain', country, ignore.case = T) ~ 'Bahrain',
                                   grepl('bangladesh', country, ignore.case = T) ~ 'Bangladesh',
                                   grepl('botswana', country, ignore.case = T) ~ 'Botswana',
                                   grepl('camer', country, ignore.case = T) ~ 'Cameroon',
                                   grepl('المغرب', country, ignore.case = T) | grepl('Maroc', country, ignore.case = T) ~ 'Morocco',
                                   grepl('jordan', country, ignore.case = T) ~ 'Jordan',
                                   grepl('ليبيا', country, ignore.case = T) ~ 'Libya',
                                   grepl('مصر', country, ignore.case = T) ~ 'Egypt',
                                   grepl('mark', country, ignore.case = T) ~ 'Denmark',
                                   grepl('salvador', country, ignore.case = T) ~ 'El Salvador',
                                   grepl('estonia', country, ignore.case = T) ~ 'Estonia',
                                   grepl('korea', country, ignore.case = T) | grepl('Güney Kore', country, ignore.case = T) ~ 'South Korea',
                                   grepl('hungary', country, ignore.case = T) ~ 'Hungary',
                                   grepl('maurice', country, ignore.case = T) ~ 'Mauritius',
                                   grepl('jamaica', country, ignore.case = T) ~ 'Jamaica',
                                   grepl('kenia', country, ignore.case = T) ~ 'Kenya',
                                   grepl('laos', country, ignore.case = T) ~ 'Laos',
                                   grepl('latvia', country, ignore.case = T) ~ 'Latvia',
                                   grepl('malta', country, ignore.case = T) ~ 'Malta',
                                   grepl('myanmar', country, ignore.case = T) ~ 'Myanmar',
                                   grepl('nepal', country, ignore.case = T) ~ 'Nepal',
                                   grepl('^oman$', country, ignore.case = T) ~ 'Oman',
                                   grepl('qatar', country, ignore.case = T) ~ 'Qatar',
                                   grepl('panam', country, ignore.case = T) ~ 'Panama',
                                   grepl('tanzania', country, ignore.case = T) ~ 'United Republic of Tanzania',
                                   grepl('vietnam', country, ignore.case = T) ~ 'Vietnam'))
# tallying un-recoded countries to see missing ones
country_counts <- dt4newVars %>%
  filter(is.na(coded_country)) %>%
  group_by(country) %>%
  tally()

Action needed:
We currently have 712 different free text country responses. The most recent codes leaves 305 responses are still not consolidated.

Political Orientation

Political orientation was measured per language. We merge these variables here.

# clean-up country coding:
rm(country_counts)

# political orientation
dt4newVars <- dt4newVars %>%
  mutate(PolOrX = labelled(rowSums(select(., ends_with("_x")), na.rm = T), 
                           labels = NULL, label="Political Compass X-Coordinate"),
         PolOrY = labelled(rowSums(select(., ends_with("_y")), na.rm = T), 
                           labels = NULL, label="Political Compass Y-Coordinate"),
         PolOrAuthoritarianLeft = rowSums(select(., ends_with("_Authoritarian_Left")), na.rm = T),
         PolOrAuthoritarianLeftLab = dplyr::recode(PolOrAuthoritarianLeft, `1` =  "Authoritarian Left", `0` = ""),
         PolOrAuthoritarianRight = rowSums(select(., ends_with("_Authoritarian_right")), na.rm = T),
         PolOrAuthoritarianRightLab = dplyr::recode(PolOrAuthoritarianRight, `1` =  "Authoritarian Right", `0` = ""),
         PolOrLibertarianLeft = rowSums(select(., ends_with("_Libertarian_Left")), na.rm = T),
         PolOrLibertarianLeftLab = dplyr::recode(PolOrLibertarianLeft, `1` =  "Libertarian Left", `0` = ""),
         PolOrLibertarianRight = rowSums(select(., ends_with("_Libertarian_Right")), na.rm = T),
         PolOrLibertarianRightLab = dplyr::recode(PolOrLibertarianRight, `1` =  "Libertarian Right", `0` = ""),
         PolOrOther = rowSums(select(., ends_with("_Other")), na.rm = T),
         PolOrOtherLab = dplyr::recode(PolOrOther, `1` =  "Other", `0` = ""),
         PolOrCat = paste0(PolOrAuthoritarianLeftLab, 
                           PolOrAuthoritarianRightLab,
                           PolOrLibertarianLeftLab,
                           PolOrLibertarianRightLab,
                           PolOrOtherLab), 
         PolOrCat = as.factor(na_if(PolOrCat, ""))) %>%
  select(-starts_with("Pol"),
         PolOrX,
         PolOrY,
         PolOrCat)
attr(dt4newVars$PolOrCat,'label') <- 'Political Orientation Quadrant'

Affect

High Arousal Negative

# High Arousal Negative
## Anger not measured in wave 1
pairs.panels.new(dt4newVars %>% select(affAnx, affNerv))

cat("<br>")


dt4newVars$affHighNeg.m <- scoreItems(keys=c(1,1), items = dt4newVars %>% select(affAnx, affNerv), min = 1, max = 5)$scores

as.data.frame(psych::describe(dt4newVars$affHighNeg.m, skew=F)) %>%
  mutate(vars = "High Arousal Negative Affect") %>%
  kable(., caption = "High Arousal Negative Affect: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
High Arousal Negative Affect: Scale Descriptives
vars n mean sd min max range se
High Arousal Negative Affect 14401 2.896 1.04 1 5 4 0.0087 
dt4newVars$affHighNeg.c <- scale(dt4newVars$affHighNeg.m, scale = F, center = T)
dt4newVars$affHighNeg.z <- scale(dt4newVars$affHighNeg.m, scale = T)
dt4newVars$affHighNeg.fa <- fa(dt4newVars %>% select(affAnx, affNerv))$scores

Low Arousal Negative Affect

# Low Arousal Negative Affect
ia.affLowNeg <- dt4newVars %>%
    dplyr::select(affBor, affExh, affDepr) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.affLowNeg$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.57 . Furthermore, deleting item(s) 1 may improve reliability.

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.affLowNeg)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
affDepr 0.4732 0.8283 2.199 1.152
affExh 0.3840 0.5209 2.524 1.230
affBor 0.2866 0.3594 2.501 1.295 
cat("<br>")


as.data.frame(psych::describe(dt4newVars %>% select(affBor, affExh, affDepr))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Low Arousal Negative Affect: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Low Arousal Negative Affect: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
affBor 12344 2.501 1.295 2 2.401 1.483 1 5 4 0.3917 -1.0254 0.0117
affExh 12327 2.524 1.230 2 2.457 1.483 1 5 4 0.3094 -1.0094 0.0111
affDepr 12341 2.199 1.152 2 2.079 1.483 1 5 4 0.6651 -0.5391 0.0104 
cat("<br>")


pairs.panels.new(dt4newVars %>% select(affBor, affExh, affDepr))

cat("<br>")


dt4newVars$affLowNeg.m <- scoreItems(keys=c(1,1,1), items = dt4newVars %>% select(affBor, affExh, affDepr), min = 1, max = 5)$scores

as.data.frame(psych::describe(dt4newVars$affLowNeg.m, skew=F)) %>%
  mutate(vars = "Low Arousal Negative Affect") %>%
  kable(., caption = "Low Arousal Negative Affect: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Low Arousal Negative Affect: Scale Descriptives
vars n mean sd min max range se
Low Arousal Negative Affect 14401 2.349 0.8433 1 5 4 0.007 
dt4newVars$affLowNeg.c <- scale(dt4newVars$affLowNeg.m, scale = F, center = T)
dt4newVars$affLowNeg.z <- scale(dt4newVars$affLowNeg.m, scale = T)
dt4newVars$affLowNeg.fa <- fa(dt4newVars %>% select(affBor, affExh, affDepr))$scores

Low Arousal Positive Affect

# Low Arousal Positive Affect
ia.affLowPos <- dt4newVars %>%
    dplyr::select(affCalm, affContent, affRel) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.affLowPos$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.74 . Furthermore, deleting item(s) 2 may improve reliability.

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.affLowPos)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
affRel 0.6268 0.8116 2.528 1.099
affCalm 0.6159 0.7853 2.792 1.069
affContent 0.4463 0.5058 2.574 1.099 
cat("<br>")


as.data.frame(psych::describe(dt4newVars %>% select(affCalm, affContent, affRel))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Low Arousal Positive Affect: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Low Arousal Positive Affect: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
affCalm 12329 2.792 1.069 3 2.811 1.483 1 5 4 0.0041 -0.7403 0.0096
affContent 12318 2.574 1.099 3 2.551 1.483 1 5 4 0.1428 -0.8217 0.0099
affRel 12324 2.528 1.099 3 2.494 1.483 1 5 4 0.2130 -0.7984 0.0099 
cat("<br>")


pairs.panels.new(dt4newVars %>% select(affCalm, affContent, affRel))

cat("<br>")


dt4newVars$affLowPos.m <- scoreItems(keys=c(1,1,1), items = dt4newVars %>% select(affCalm, affContent, affRel), min = 1, max = 5)$scores

as.data.frame(psych::describe(dt4newVars$affLowPos.m, skew=F)) %>%
  mutate(vars = "Low Arousal Positive Affect") %>%
  kable(., caption = "Low Arousal Positive Affect: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Low Arousal Positive Affect: Scale Descriptives
vars n mean sd min max range se
Low Arousal Positive Affect 14401 2.685 0.8263 1 5 4 0.0069 
dt4newVars$affLowPos.c <- scale(dt4newVars$affLowPos.m, scale = F, center = T)
dt4newVars$affLowPos.z <- scale(dt4newVars$affLowPos.m, scale = T)
dt4newVars$affLowPos.fa <- fa(dt4newVars %>% select(affCalm, affContent, affRel))$scores

High Arousal Positive Affect

# High Arousal Positive Affect
ia.affHighPos <- dt4newVars %>%
    dplyr::select(affEnerg, affExc, affInsp) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.affHighPos$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.67 .

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.affHighPos)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
affInsp 0.5265 0.7258 2.308 1.142
affEnerg 0.4898 0.6415 2.378 1.077
affExc 0.4410 0.5522 2.099 1.113 
cat("<br>")


as.data.frame(psych::describe(dt4newVars %>% select(affEnerg, affExc, affInsp))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "High Arousal Positive Affect: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
High Arousal Positive Affect: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
affEnerg 12318 2.378 1.077 2 2.314 1.483 1 5 4 0.3196 -0.7006 0.0097
affExc 12315 2.099 1.113 2 1.969 1.483 1 5 4 0.6913 -0.5068 0.0100
affInsp 12327 2.308 1.142 2 2.217 1.483 1 5 4 0.4658 -0.7418 0.0103 
cat("<br>")


pairs.panels.new(dt4newVars %>% select(affEnerg, affExc, affInsp))

cat("<br>")


dt4newVars$affHighPos.m <- scoreItems(keys=c(1,1,1), items = dt4newVars %>% select(affEnerg, affExc, affInsp), min = 1, max = 5)$scores

as.data.frame(psych::describe(dt4newVars$affHighPos.m, skew=F)) %>%
  mutate(vars = "High Arousal Positive Affect") %>%
  kable(., caption = "High Arousal Positive Affect: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
High Arousal Positive Affect: Scale Descriptives
vars n mean sd min max range se
High Arousal Positive Affect 14401 2.224 0.8098 1 5 4 0.0067 
dt4newVars$affHighPos.c <- scale(dt4newVars$affHighPos.m, scale = F, center = T)
dt4newVars$affHighPos.z <- scale(dt4newVars$affHighPos.m, scale = T)
dt4newVars$affHighPos.fa <- fa(dt4newVars %>% select(affEnerg, affExc, affInsp))$scores

Loneliness

ia.lone<- dt4newVars %>%
    dplyr::select(starts_with("lone")) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.lone$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.8 .

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.lone)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
lone01 0.6827 0.8219 2.332 1.133
lone02 0.6372 0.7399 2.642 1.217
lone03 0.6196 0.7115 1.945 1.092 
cat("<br>")


as.data.frame(psych::describe(dt4newVars %>% select(starts_with("lone")))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Loneliness: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Loneliness: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
lone01 12219 2.332 1.133 2 2.251 1.483 1 5 4 0.4004 -0.7876 0.0102
lone02 12208 2.642 1.217 3 2.599 1.483 1 5 4 0.1507 -1.0071 0.0110
lone03 12204 1.945 1.092 2 1.774 1.483 1 5 4 0.9601 0.0146 0.0099 
cat("<br>")


pairs.panels.new(dt4newVars %>% select(starts_with("lone")))

cat("<br>")


dt4newVars$lone.m <- scoreItems(keys=c(1,1,1), items = dt4newVars %>% select(starts_with("lone")), min = 1, max = 5)$scores

as.data.frame(psych::describe(dt4newVars$lone.m, skew=F)) %>%
  mutate(vars = "Loneliness") %>%
  kable(., caption = "Loneliness: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Loneliness: Scale Descriptives
vars n mean sd min max range se
Loneliness 14401 2.311 0.8961 1 5 4 0.0075 
dt4newVars$lone.c <- scale(dt4newVars$lone.m, scale = F, center = T)
dt4newVars$lone.z <- scale(dt4newVars$lone.m, scale = T)
dt4newVars$lone.fa <- fa(dt4newVars %>% select(starts_with("lone")))$scores

Boredom

ia.bor<- dt4newVars %>%
    dplyr::select(starts_with("bor0"), -bor03) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.bor$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.51 . Item(s) that exhibited low correlation with the rest of the scale were: 3 . Furthermore, deleting item(s) 3 may improve reliability.

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.bor)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
bor02 0.4888 0.7379 0.1007 1.878
bor01 0.4635 0.7304 0.4189 1.902
bor03_R 0.0776 0.4080 -0.2824 1.681 
pairs.panels.new(dt4newVars %>% select(starts_with("bor0"), -bor03))

Item dropped:
Item three was not well behaved. It seems to measure something else. We dropped it for now.

pairs.panels.new(dt4newVars %>% select(starts_with("bor0"), -bor03, -bor03_R))

cat("<br>")


dt4newVars$bor.m <- scoreItems(keys=c(1,1), items = dt4newVars %>% select(starts_with("bor0"), -bor03, -bor03_R), min = -3, max = 3)$scores

as.data.frame(psych::describe(dt4newVars$bor.m, skew=F)) %>%
  mutate(vars = "Boredom") %>%
  kable(., caption = "Boredom: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Boredom: Scale Descriptives
vars n mean sd min max range se
Boredom 14401 0.2134 1.548 -3 3 6 0.0129 
dt4newVars$bor.c <- scale(dt4newVars$bor.m, scale = F, center = T)
dt4newVars$bor.z <- scale(dt4newVars$bor.m, scale = T)
dt4newVars$bor.fa <- fa(dt4newVars %>% select(starts_with("bor0"), -bor03, -bor03_R))$scores

Isolation

cat(crayon::bold("Offline Isolation"))

Offline Isolation

ia.isoPers <- dt4newVars %>%
    dplyr::select(ends_with("inPerson")) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.isoPers$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.58 .

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.isoPers)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
isoOthPpl_inPerson 0.4618 0.7605 1.955 2.205
isoImmi_inPerson 0.3782 0.5165 0.501 1.419
isoFriends_inPerson 0.3316 0.4334 2.067 2.482 
cat("<br>")


as.data.frame(psych::describe(dt4newVars %>% select(ends_with("inPerson")))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Isolation offline: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Isolation offline: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
isoFriends_inPerson 12158 2.067 2.482 1 1.708 1.483 0 7 7 0.9742 -0.4959 0.0225
isoOthPpl_inPerson 12105 1.955 2.205 1 1.597 1.483 0 7 7 1.0423 -0.0553 0.0200
isoImmi_inPerson 12033 0.501 1.419 0 0.087 0.000 0 7 7 3.2382 10.0072 0.0129 
cat("<br>")


pairs.panels.new(dt4newVars %>% select(ends_with("inPerson")))

cat("<br>")


dt4newVars$isoPers.m <- scoreItems(keys=c(1,1,1), items = dt4newVars %>% select(ends_with("inPerson")), min = 0, max = 7)$scores

as.data.frame(psych::describe(dt4newVars$isoPers.m, skew=F)) %>%
  mutate(vars = "Isolation offline") %>%
  kable(., caption = "Isolation offline: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Isolation offline: Scale Descriptives
vars n mean sd min max range se
Isolation offline 14401 1.374 1.439 0 7 7 0.012 
dt4newVars$isoPers.c <- scale(dt4newVars$isoPers.m, scale = F, center = T)
dt4newVars$isoPers.z <- scale(dt4newVars$isoPers.m, scale = T)
dt4newVars$isoPers.fa <- fa(dt4newVars %>% select(ends_with("inPerson")))$scores

cat(crayon::bold("Online Isolation"))

Online Isolation

ia.isoOnl <- dt4newVars %>%
    dplyr::select(ends_with("online")) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.isoOnl$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.54 .

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.isoOnl)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
isoOthPpl_online 0.4658 0.9355 2.9763 2.730
isoFriends_online 0.3011 0.3776 4.8793 2.347
isoImmi_online 0.2990 0.3746 0.8074 1.885 
cat("<br>")


as.data.frame(psych::describe(dt4newVars %>% select(ends_with("inPerson")))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Isolation online: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Isolation online: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
isoFriends_inPerson 12158 2.067 2.482 1 1.708 1.483 0 7 7 0.9742 -0.4959 0.0225
isoOthPpl_inPerson 12105 1.955 2.205 1 1.597 1.483 0 7 7 1.0423 -0.0553 0.0200
isoImmi_inPerson 12033 0.501 1.419 0 0.087 0.000 0 7 7 3.2382 10.0072 0.0129 
cat("<br>")


pairs.panels.new(dt4newVars %>% select(ends_with("online")))

cat("<br>")


dt4newVars$isoOnl.m <- scoreItems(keys=c(1,1,1), items = dt4newVars %>% select(ends_with("online")), min = 0, max = 7)$scores

as.data.frame(psych::describe(dt4newVars$isoPers.m, skew=F)) %>%
  mutate(vars = "Isolation online") %>%
  kable(., caption = "Isolation online: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Isolation online: Scale Descriptives
vars n mean sd min max range se
Isolation online 14401 1.374 1.439 0 7 7 0.012 
dt4newVars$isoOnl.c <- scale(dt4newVars$isoOnl.m, scale = F, center = T)
dt4newVars$isoOnl.z <- scale(dt4newVars$isoOnl.m, scale = T)
dt4newVars$isoOnl.fa <- fa(dt4newVars %>% select(ends_with("online")))$scores

# Leave House
as.data.frame(psych::describe(dt4newVars$houseLeave, skew=F)) %>%
  mutate(vars = "Leaving House") %>%
  kable(., caption = "Leaving House: Item Descriptive", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Leaving House: Item Descriptive
vars n mean sd min max range se
Leaving House 11876 2.438 1.07 1 4 3 0.0098

Government Response

No responses yet

# extC19Msg

Community Response

ia.ext <- dt4newVars %>%
    dplyr::select(starts_with("extC19"), -extC19Msg) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.ext$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.77 .

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.ext)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
extC19Rules 0.7054 0.9270 4.050 1.456
extC19Org 0.5827 0.6724 3.743 1.432
extC19Punish 0.5358 0.6037 3.140 1.669 
cat("<br>")


as.data.frame(psych::describe(dt4newVars %>% select(starts_with("extC19"), -extC19Msg))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Community response: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Community response: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
extC19Rules 11967 4.050 1.456 4 4.138 1.483 1 6 5 -0.4138 -0.7261 0.0133
extC19Punish 11962 3.140 1.669 3 3.051 1.483 1 6 5 0.2305 -1.1750 0.0153
extC19Org 11967 3.743 1.432 4 3.774 1.483 1 6 5 -0.1904 -0.7870 0.0131 
cat("<br>")


pairs.panels.new(dt4newVars %>% select(starts_with("extC19"), -extC19Msg))

cat("<br>")


dt4newVars$ext.m <- scoreItems(keys=c(1,1,1), items = dt4newVars %>% select(starts_with("extC19"), -extC19Msg), 
                             min = 1, max = 6)$scores

as.data.frame(psych::describe(dt4newVars$ext.m, skew=F)) %>%
  mutate(vars = "Community response") %>%
  kable(., caption = "Community response: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Community response: Scale Descriptives
vars n mean sd min max range se
Community response 14401 3.648 1.147 1 6 5 0.0096 
dt4newVars$ext.c <- scale(dt4newVars$ext.m, scale = F, center = T)
dt4newVars$ext.z <- scale(dt4newVars$ext.m, scale = T)
dt4newVars$ext.fa <- fa(dt4newVars %>% select(starts_with("extC19"), -extC19Msg))$scores

Behavioral Response

ia.beh <- dt4newVars %>%
    dplyr::select(starts_with("c19per")) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.beh$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.68 .

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.beh)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
c19perBeh02 0.6036 0.9016 2.622 0.8603
c19perBeh03 0.4663 0.5644 2.073 1.3758
c19perBeh01 0.4304 0.5100 2.468 0.9603 
cat("<br>")


as.data.frame(psych::describe(dt4newVars %>% select(starts_with("c19per")))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Behavioral response: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Behavioral response: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
c19perBeh01 12009 2.468 0.9603 3 2.684 0 -3 3 6 -2.879 10.787 0.0088
c19perBeh02 12012 2.622 0.8603 3 2.816 0 -3 3 6 -3.577 16.371 0.0078
c19perBeh03 12013 2.073 1.3758 3 2.375 0 -3 3 6 -1.862 3.236 0.0126 
cat("<br>")


pairs.panels.new(dt4newVars %>% select(starts_with("c19per")))

cat("<br>")


dt4newVars$beh.m <- scoreItems(keys=c(1,1,1), items = dt4newVars %>% select(starts_with("c19per")), 
                             min = -3, max = 3)$scores

as.data.frame(psych::describe(dt4newVars$beh.m, skew=F)) %>%
  mutate(vars = "Behavioral response") %>%
  kable(., caption = "Behavioral response: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Behavioral response: Scale Descriptives
vars n mean sd min max range se
Behavioral response 14401 2.489 0.8045 -3 3 6 0.0067 
dt4newVars$beh.c <- scale(dt4newVars$beh.m, scale = F, center = T)
dt4newVars$beh.z <- scale(dt4newVars$beh.m, scale = T)
dt4newVars$beh.fa <- fa(dt4newVars %>% select(starts_with("c19per")))$scores

Hope and Efficacy

as.data.frame(psych::describe(dt4newVars$c19Hope)) %>%
  mutate(vars = "Hope") %>%
  kable(., caption = "Hope: Item Descriptive", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Hope: Item Descriptive
vars n mean sd median trimmed mad min max range skew kurtosis se
Hope 12039 1.038 1.622 1 1.211 1.483 -3 3 6 -0.7986 -0.2141 0.0148 
ggplot(dt4newVars, aes(x = c19Hope)) +
  geom_histogram(binwidth=1, alpha=0.5) +
  #geom_density(alpha=0.6)+
  labs(title="Hope distribution",x="Corona Virus Hope", y = "Frequency") +
  theme_Publication()

as.data.frame(psych::describe(dt4newVars$c19Eff)) %>%
  mutate(vars = "Efficacy") %>%
  kable(., caption = "Efficacy: Item Descriptive", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Efficacy: Item Descriptive
vars n mean sd median trimmed mad min max range skew kurtosis se
Efficacy 12035 0.7914 1.658 1 0.9204 1.483 -3 3 6 -0.6522 -0.4741 0.0151 
ggplot(dt4newVars, aes(x = c19Eff)) +
  geom_histogram(binwidth=1, alpha=0.5) +
  #geom_density(alpha=0.6)+
  labs(title="Efficacy distribution",x="Corona Virus Efficacy", y = "Frequency") +
  theme_Publication()

State Paranoia

ia.para <- dt4newVars %>%
    dplyr::select(starts_with("para")) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.para$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.71 . Furthermore, deleting item(s) 1 may improve reliability.

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.para)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
para02 0.6332 0.8824 2.381 2.600
para03 0.5701 0.7143 2.325 2.623
para01 0.4008 0.4533 5.282 3.014 
cat("<br>")


as.data.frame(psych::describe(dt4newVars %>% select(starts_with("para")))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "State Paranoia: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
State Paranoia: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
para01 11712 5.282 3.014 6 5.359 2.965 0 10 10 -0.2445 -0.9668 0.0278
para02 11708 2.381 2.600 2 1.988 2.965 0 10 10 1.0278 0.2353 0.0240
para03 11707 2.325 2.623 1 1.913 1.483 0 10 10 1.0573 0.2038 0.0242 
cat("<br>")


pairs.panels.new(dt4newVars %>% select(starts_with("para")))

cat("<br>")


dt4newVars$para.m <- scoreItems(keys=c(1,1,1), items = dt4newVars %>% select(starts_with("para")), 
                             min = 0, max = 10)$scores

as.data.frame(psych::describe(dt4newVars$para.m, skew=F)) %>%
  mutate(vars = "State Paranoia") %>%
  kable(., caption = "State Paranoia: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
State Paranoia: Scale Descriptives
vars n mean sd min max range se
State Paranoia 14401 3.268 1.976 0 10 10 0.0165 
dt4newVars$para.c <- scale(dt4newVars$para.m, scale = F, center = T)
dt4newVars$para.z <- scale(dt4newVars$para.m, scale = T)
dt4newVars$para.fa <- fa(dt4newVars %>% select(starts_with("para")))$scores

Conspiracy Theory

ia.consp <- dt4newVars %>%
    dplyr::select(starts_with("consp")) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.consp$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.69 . Furthermore, deleting item(s) 3 may improve reliability.

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.consp)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
consp02 0.5997 0.8273 7.200 2.479
consp01 0.5749 0.7564 6.888 2.616
consp03 0.3693 0.4205 5.325 2.735 
cat("<br>")


as.data.frame(psych::describe(dt4newVars %>% select(starts_with("consp")))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Conspiracy Theory: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Conspiracy Theory: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
consp01 11705 6.888 2.616 7 7.168 2.965 0 10 10 -0.6867 -0.2185 0.0242
consp02 11694 7.200 2.479 8 7.504 2.965 0 10 10 -0.8578 0.1660 0.0229
consp03 11696 5.325 2.735 5 5.371 2.965 0 10 10 -0.1192 -0.7538 0.0253 
cat("<br>")


pairs.panels.new(dt4newVars %>% select(starts_with("consp")))

cat("<br>")


dt4newVars$consp.m <- scoreItems(keys=c(1,1,1), items = dt4newVars %>% select(starts_with("consp")), 
                             min = 0, max = 10)$scores

as.data.frame(psych::describe(dt4newVars$consp.m, skew=F)) %>%
  mutate(vars = "Conspiracy Theory") %>%
  kable(., caption = "Conspiracy Theory: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Conspiracy Theory: Scale Descriptives
vars n mean sd min max range se
Conspiracy Theory 14401 6.508 1.859 0 10 10 0.0155 
dt4newVars$para.c <- scale(dt4newVars$consp.m, scale = F, center = T)
dt4newVars$para.z <- scale(dt4newVars$consp.m, scale = T)
dt4newVars$para.fa <- fa(dt4newVars %>% select(starts_with("consp")))$scores

Job Insecurity

ia.jobinsec<- dt4newVars %>%
    dplyr::select(starts_with("jbInsec"), -jbInsec02, -jbInsec04) %>%
    na_if(., -99) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.jobinsec$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.85 .

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.jobinsec)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
jbInsec01 0.7612 0.8769 -0.7671 1.178
jbInsec03 0.7168 0.8014 -0.0667 1.381
jbInsec02_R 0.6848 0.7530 -0.7225 1.163 
cat("<br>")


as.data.frame(psych::describe(dt4newVars %>% select(starts_with("jbInsec"), -jbInsec02, -jbInsec04) %>% na_if(., -99))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Job insecurity: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Job insecurity: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
jbInsec01 8991 -0.7671 1.178 -1 -0.9101 1.483 -2 2 4 0.7677 -0.2545 0.0124
jbInsec03 9773 -0.0667 1.381 0 -0.0834 1.483 -2 2 4 0.0212 -1.3020 0.0140
jbInsec02_R 9277 -0.7225 1.163 -1 -0.8482 1.483 -2 2 4 0.6778 -0.3598 0.0121 
cat("<br>")


pairs.panels.new(dt4newVars %>% select(starts_with("jbInsec"), -jbInsec02, -jbInsec04) %>% na_if(., -99))

cat("<br>")


dt4newVars$jobinsec.m <- scoreItems(keys=c(1,1,1), 
                                  items = dt4newVars %>% select(starts_with("jbInsec"), -jbInsec02, -jbInsec04) %>% na_if(., -99),
                                  min = -2, max = 2)$scores

as.data.frame(psych::describe(dt4newVars$jobinsec.m, skew=F)) %>%
  mutate(vars = "Job insecurity") %>%
  kable(., caption = "Job insecurity: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Job insecurity: Scale Descriptives
vars n mean sd min max range se
Job insecurity 14401 -0.5737 0.8535 -2 2 4 0.0071 
dt4newVars$jobinsec.c <- scale(dt4newVars$jobinsec.m, scale = F, center = T)
dt4newVars$jobinsec.z <- scale(dt4newVars$jobinsec.m, scale = T)
dt4newVars$jobinsec.fa <- fa(dt4newVars %>% select(starts_with("jbInsec"), -jbInsec02, -jbInsec04))$scores

Financial Strain

ia.pfs<- dt4newVars %>%
    dplyr::select(starts_with("PFS0")) %>%
    Scale::Scale() %>%
    Scale::ItemAnalysis()
ia.pfs$rely

Reliability Analysis of . ScaleData object.

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.85 . Furthermore, deleting item(s) 2 may improve reliability.

cat("<br><br>A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:")



A gls factor analysis was conducted. Items were regressed to a single factor. Their loadings are the following:

as.data.frame(Scale::ReportTable(ia.pfs)) %>%
  kable(., row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Item Corr. to scale Factor Loading Mean SD
PFS01 0.7994 0.9361 -0.2150 1.250
PFS03 0.7597 0.8602 -0.4728 1.249
PFS02 0.6109 0.6462 0.4447 1.212 
cat("<br>")


as.data.frame(psych::describe(dt4newVars %>% select(starts_with("PFS0")))) %>%
  mutate(vars = rownames(.)) %>%
  kable(., caption = "Financial Strain: Item Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
Financial Strain: Item Descriptives
vars n mean sd median trimmed mad min max range skew kurtosis se
PFS01 12185 -0.2150 1.250 0 -0.2687 1.483 -2 2 4 0.184 -1.0404 0.0113
PFS02 12193 0.4447 1.212 1 0.5441 1.483 -2 2 4 -0.581 -0.6659 0.0110
PFS03 12194 -0.4728 1.249 -1 -0.5654 1.483 -2 2 4 0.449 -0.8835 0.0113 
cat("<br>")


pairs.panels.new(dt4newVars %>% select(starts_with("PFS0")))

cat("<br>")


dt4newVars$pfs.m <- scoreItems(keys=c(1,1,1), 
                                  items = dt4newVars %>% select(starts_with("PFS0")),
                                  min = -2, max = 2)$scores

as.data.frame(psych::describe(dt4newVars$pfs.m, skew=F)) %>%
  mutate(vars = "inancial Strain") %>%
  kable(., caption = "inancial Strain: Scale Descriptives", row.names = FALSE) %>% 
  kable_styling("hover", full_width = F, latex_options = "hold_position")
inancial Strain: Scale Descriptives
vars n mean sd min max range se
inancial Strain 14401 -0.0686 1 -2 2 4 0.0083 
dt4newVars$pfs.c <- scale(dt4newVars$pfs.m, scale = F, center = T)
dt4newVars$pfs.z <- scale(dt4newVars$pfs.m, scale = T)
dt4newVars$pfs.fa <- fa(dt4newVars %>% select(starts_with("PFS0")))$scores

Reduce to Relevant Variales

# clean-up Item Analyses
rm(list=ls(pattern="ia"))

# remove directly identifiable data (with and without page timers)
dt5ReducedTimer <- dt4newVars %>%
  select(-c(IPAddress, 
            RecipientLastName, 
            RecipientFirstName, 
            RecipientEmail, 
            ExternalReference, 
            LocationLatitude, 
            LocationLongitude,
            DistributionChannel,
            ICRec_1_TEXT))
dt5Reduced <- dt5ReducedTimer %>%
  select(-starts_with("t_"))

# remove filtered cases (with and without page timers)
dt5ReducedTimerCases <- dt5ReducedTimer %>%
  filter(FilterPreview == 0,
         FilterTime == 0,
         FilterStraightliner == 0) %>%
  select(-starts_with("Filter"))
dt5ReducedCases <- dt5Reduced %>%
  filter(FilterPreview == 0,
         FilterTime == 0,
         FilterStraightliner == 0) %>%
  select(-starts_with("Filter"))

Export

Export main dataframe as RData and SPSS sav files. We export versions with and without page timers

namSPSS <- paste0("data/cleaned data/Psycorona Baseline cleaned ", format(Sys.time(), format = "%F %H-%M %Z"),".sav")
namR <- paste0("data/cleaned data/Psycorona Baseline cleaned ", format(Sys.time(), format = "%F %H-%M %Z"),".RData")
namTSPSS <- paste0("data/cleaned data/Psycorona Baseline cleaned with page timer ", format(Sys.time(), format = "%F %H-%M %Z"),".sav")
namTR <- paste0("data/cleaned data/Psycorona Baseline cleaned with page timer ", format(Sys.time(), format = "%F %H-%M %Z"),".RData")
write_sav(dt5Reduced, namSPSS)
write_sav(dt5ReducedTimer, namTSPSS)
save(dt5Reduced, file = namR)
save(dt5ReducedTimer, file = namTR)
rm(list=ls(pattern="nam"))

# export for Shiny
saveRDS(dt5ReducedCases, file = "../PsyCorona-WebApp/data/reducedData.rds")